ArticleTranslational vision science & technology2025
Grading of Foveal Hypoplasia Using Deep Learning on Retinal Fundus Images.
Article in Translational vision science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: This study aimed to develop and evaluate a deep learning model for grading foveal hypoplasia using retinal fundus images. Methods: This retrospective study included patients with foveal developmental disorders, using color fundus images and optical coherence tomography scans taken between January 1, 2001, and August 31, 2021. In total, 605 retinal fundus images were obtained from 303 patients (male, 55.1%; female, 44.9%). After augmentation, the training, validation, and testing data sets comprised 1229, 527, and 179 images, respectively. A deep learning model was developed for binary classification (normal vs. abnormal foveal development) and six-grade classification of foveal hypoplasia. The outcome was compared with those by senior and junior clinicians. Results: Higher grade of foveal hypoplasia showed worse visual outcomes (P < 0.001). The binary classification achieved a best testing accuracy of 84.36% using the EfficientNet_b1 model, with 84.51% sensitivity and 84.26% specificity. The six-grade classification achieved a best testing accuracy of 78.21% with the model. The model achieved an area under the receiver operating characteristic curve (AUROC) of 0.9441 and an area under the precision-recall curve (AUPRC) of 0.9654 (both P < 0.0001) in the validation set and an AUROC of 0.8777 and an AUPRC of 0.8327 (both P < 0.0001) in the testing set. Compared to junior and senior clinicians, the EfficientNet_b1 model exhibited a superior performance in both binary and six-grade classification (both P < 0.00001). Conclusions: The deep learning model in this study proved more efficient and accurate than assessments by junior and senior clinicians for identifying foveal developmental diseases in retinal fundus images. With the aid of the model, we were able to accurately assess patients with foveal developmental disorders. Translational Relevance: This study strengthened the importance for a pediatric deep learning system to support clinical evaluation, particularly in cases reliant on retinal fundus images.
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